BAROMÈTRE SANTÉ JEUNE 2019 (BSJ-2019)

BAROMÈTRE SANTÉ JEUNE 2019 (BSJ-2019)

Source : https://data.gouv.nc/explore/dataset/bsj-2019/information

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Building wheels for collected packages: metaphone, pystache, python-Levenshtein
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?25h  Created wheel for pystache: filename=pystache-0.6.0-py3-none-any.whl size=83618 sha256=0907f0620b267aa556481fd4894e6f2795dfe6df9514953b0db4a68a46028169
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?25h  Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp38-cp38-linux_x86_64.whl size=184990 sha256=a1c2c3730eebf0c504a4081af5329f0f08b30e45dda95f898917b87a8b118962
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Successfully built metaphone pystache python-Levenshtein
Installing collected packages: regex, python-stdnum, python-crfsuite, ply, metaphone, Werkzeug, typing-extensions, tqdm, toolz, scipy, python-Levenshtein, pystache, multidict, locket, jsonpath-ng, itsdangerous, fsspec, frozenlist, cloudpickle, async-timeout, yarl, varname, pydantic, partd, nltk, flask, bokeh, aiosignal, wordcloud, flask_cors, dask, aiohttp, dataprep
  Attempting uninstall: scipy
    Found existing installation: scipy 1.8.1
    Uninstalling scipy-1.8.1:
      Successfully uninstalled scipy-1.8.1
Successfully installed Werkzeug-2.1.2 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 bokeh-2.4.3 cloudpickle-2.1.0 dask-2021.12.0 dataprep-0.4.3 flask-2.1.2 flask_cors-3.0.10 frozenlist-1.3.0 fsspec-2022.5.0 itsdangerous-2.1.2 jsonpath-ng-1.5.3 locket-1.0.0 metaphone-0.6 multidict-6.0.2 nltk-3.7 partd-1.2.0 ply-3.11 pydantic-1.9.1 pystache-0.6.0 python-Levenshtein-0.12.2 python-crfsuite-0.9.8 python-stdnum-1.17 regex-2021.11.10 scipy-1.7.1 toolz-0.11.2 tqdm-4.64.0 typing-extensions-4.2.0 varname-0.8.3 wordcloud-1.8.1 yarl-1.7.2
WARNING: There was an error checking the latest version of pip.

!wget "https://data.gouv.nc/explore/dataset/bsj-2019/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/barometre_jeune_2019.csv
    
--2022-05-24 05:21:53--  https://data.gouv.nc/explore/dataset/bsj-2019/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 
13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443... 
connected.
HTTP request sent, awaiting response... 
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/barometre_jeune_2019.csv’


          data/baro     [<=>                 ]       0  --.-KB/s               
         data/barom     [ <=>                ]  36.70K   183KB/s               
        data/barome     [  <=>               ] 100.69K   250KB/s               
       data/baromet     [   <=>              ] 212.65K   352KB/s               
      data/barometr     [    <=>             ] 420.61K   522KB/s               
     data/barometre     [     <=>            ] 868.55K   863KB/s               
    data/barometre_     [      <=>           ]   1.72M  1.43MB/s               
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 data/barometre_jeu     [         <=>        ]   5.57M  2.85MB/s               
data/barometre_jeun     [          <=>       ]   6.44M  3.29MB/s    in 2.0s    

2022-05-24 05:21:57 (3.29 MB/s) - ‘data/barometre_jeune_2019.csv’ saved [6748346]
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from dataprep.eda import plot
df = pd.read_csv("data/barometre_jeune_2019.csv")
df.head()
N°Obs Date_Enreg Province_Etab Secteur_Etab Niveau_Scolaire CS1 CS2 CS3 Age CS5 ... PR1h PR2a PR2b PR2c PR2d PR2e PR2f PR2g PR2h Pond
0 1 2019-01-07 Province Sud Public 5EME Une fille Collège 5ème 12 Wallisienne, Futunienne ... Plutot bien informe Je ne connais pas le risque Risque plutot eleve Risque nul Je ne connais pas le risque Je ne connais pas le risque Je ne connais pas le risque Risque tres eleve Je ne connais pas le risque 14.818182
1 2 2019-01-07 Province Sud Public 6EME Un garçon Collège 6ème 12 Kanak;Wallisienne, Futunienne ... Je ne sais pas Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est 14.761290
2 3 2019-01-07 Province Sud Public 5EME Une fille Collège 5ème 13 Kanak;Wallisienne, Futunienne ... Je ne sais pas Risque plutot faible Risque plutot faible Risque plutot faible Risque plutot faible Risque plutot faible Risque plutot faible Risque plutot faible Risque plutot faible 11.261438
3 4 2019-01-07 Province Sud Public 5EME Une fille Collège 5ème 12 Kanak;Ni-Vanuatu;Autres ... Plutot bien informe Risque plutot eleve Risque tres eleve Risque plutot eleve Risque tres eleve Je ne connais pas le risque Risque tres eleve Risque tres eleve Risque tres eleve 14.818182
4 5 2019-01-07 Province Sud Public 6EME Un garçon Collège 6ème 12 Wallisienne, Futunienne ... Plutot bien informe Je ne sais pas ce que c'est Risque plutot faible Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est Je ne sais pas ce que c'est 14.761290

5 rows × 163 columns

plot(df)
DataPrep.EDA Report
Dataset Statistics
Number of Variables 163
Number of Rows 3435
Missing Cells 189125
Missing Cells (%) 33.8%
Duplicate Rows 0
Duplicate Rows (%) 0.0%
Total Size in Memory 32.4 MB
Average Row Size in Memory 9.7 KB
Variable Types
  • Numerical: 7
  • Categorical: 156
Dataset Insights
N°Obs is uniformly distributed Uniform
CS5_Autre has 3134 (91.24%) missing values Missing
VS5_Autre has 3221 (93.77%) missing values Missing
ES3 has 836 (24.34%) missing values Missing
ES3b has 2416 (70.33%) missing values Missing
ES4 has 962 (28.01%) missing values Missing
ES4b has 2439 (71.0%) missing values Missing
ES7 has 2663 (77.53%) missing values Missing
ES7_Autre has 3357 (97.73%) missing values Missing
ES8 has 1656 (48.21%) missing values Missing
Dataset Insights
ES9 has 2029 (59.07%) missing values Missing
ES9b has 2390 (69.58%) missing values Missing
AS3 has 1805 (52.55%) missing values Missing
AS3_Autre has 3227 (93.94%) missing values Missing
AL4 has 1528 (44.48%) missing values Missing
AL4_Autre has 3321 (96.68%) missing values Missing
AP3 has 487 (14.18%) missing values Missing
AP4 has 487 (14.18%) missing values Missing
AP4_Autre has 3261 (94.93%) missing values Missing
AP5 has 2950 (85.88%) missing values Missing
Dataset Insights
AP5_Autre has 3399 (98.95%) missing values Missing
SM4_Autre has 3152 (91.76%) missing values Missing
SM5 has 903 (26.29%) missing values Missing
CV4a has 1843 (53.65%) missing values Missing
CV4b has 1842 (53.62%) missing values Missing
VI2 has 2882 (83.9%) missing values Missing
VI2_Autre has 3364 (97.93%) missing values Missing
VI3 has 2883 (83.93%) missing values Missing
VI3_Autre has 3406 (99.16%) missing values Missing
VI4 has 2885 (83.99%) missing values Missing
Dataset Insights
VI4_Autre has 3384 (98.52%) missing values Missing
VI6 has 2271 (66.11%) missing values Missing
VI6_Autre has 3320 (96.65%) missing values Missing
VI7 has 2272 (66.14%) missing values Missing
VI7_Autre has 3396 (98.86%) missing values Missing
VI8 has 2273 (66.17%) missing values Missing
VI8_Autre has 3364 (97.93%) missing values Missing
TB2 has 1937 (56.39%) missing values Missing
TB3 has 1914 (55.72%) missing values Missing
TB4 has 2494 (72.61%) missing values Missing
Dataset Insights
TB5 has 2494 (72.61%) missing values Missing
TB6 has 2494 (72.61%) missing values Missing
TB7 has 2494 (72.61%) missing values Missing
AO2a has 2435 (70.89%) missing values Missing
AO2b has 2211 (64.37%) missing values Missing
AO3a has 2200 (64.05%) missing values Missing
AO3b has 2202 (64.1%) missing values Missing
AO3c has 2200 (64.05%) missing values Missing
AO3d has 2202 (64.1%) missing values Missing
AO4 has 2200 (64.05%) missing values Missing
Dataset Insights
AO5 has 2620 (76.27%) missing values Missing
AO6 has 2621 (76.3%) missing values Missing
AO7 has 2620 (76.27%) missing values Missing
AO8 has 2620 (76.27%) missing values Missing
AO9 has 2201 (64.08%) missing values Missing
AO10 has 2701 (78.63%) missing values Missing
AO11 has 2692 (78.37%) missing values Missing
CN2 has 2443 (71.12%) missing values Missing
CN3 has 2431 (70.77%) missing values Missing
CN4 has 2929 (85.27%) missing values Missing
Dataset Insights
CN5 has 2929 (85.27%) missing values Missing
CN6 has 2930 (85.3%) missing values Missing
CN7 has 2930 (85.3%) missing values Missing
AD3 has 3343 (97.32%) missing values Missing
SS1 has 40 (1.16%) missing values Missing
SS2 has 41 (1.19%) missing values Missing
SS3 has 2527 (73.57%) missing values Missing
SS4 has 2513 (73.16%) missing values Missing
SS5 has 2850 (82.97%) missing values Missing
SS6 has 2850 (82.97%) missing values Missing
Dataset Insights
SS6_Autre has 3415 (99.42%) missing values Missing
SS7 has 3149 (91.67%) missing values Missing
SS7_Autre has 3372 (98.17%) missing values Missing
SS8 has 2513 (73.16%) missing values Missing
SS9 has 3138 (91.35%) missing values Missing
SS10 has 2513 (73.16%) missing values Missing
SS11 has 48 (1.4%) missing values Missing
SS12 has 2196 (63.93%) missing values Missing
PR1a has 65 (1.89%) missing values Missing
PR1b has 65 (1.89%) missing values Missing
Dataset Insights
PR1c has 65 (1.89%) missing values Missing
PR1d has 65 (1.89%) missing values Missing
PR1e has 65 (1.89%) missing values Missing
PR1f has 65 (1.89%) missing values Missing
PR1g has 65 (1.89%) missing values Missing
PR1h has 65 (1.89%) missing values Missing
PR2a has 76 (2.21%) missing values Missing
PR2b has 76 (2.21%) missing values Missing
PR2c has 76 (2.21%) missing values Missing
PR2d has 76 (2.21%) missing values Missing
Dataset Insights
PR2e has 76 (2.21%) missing values Missing
PR2f has 76 (2.21%) missing values Missing
PR2g has 76 (2.21%) missing values Missing
PR2h has 76 (2.21%) missing values Missing
ES9 is skewed Skewed
SS3 is skewed Skewed
CS5 has a high cardinality: 103 distinct values High Cardinality
CS5_Autre has a high cardinality: 149 distinct values High Cardinality
VS5 has a high cardinality: 51 distinct values High Cardinality
VS5_Autre has a high cardinality: 178 distinct values High Cardinality
Dataset Insights
ES7 has a high cardinality: 55 distinct values High Cardinality
ES7_Autre has a high cardinality: 69 distinct values High Cardinality
AS3 has a high cardinality: 181 distinct values High Cardinality
AS3_Autre has a high cardinality: 185 distinct values High Cardinality
AL4 has a high cardinality: 71 distinct values High Cardinality
AL4_Autre has a high cardinality: 113 distinct values High Cardinality
AP4 has a high cardinality: 456 distinct values High Cardinality
AP4_Autre has a high cardinality: 170 distinct values High Cardinality
AP5 has a high cardinality: 128 distinct values High Cardinality
SM4 has a high cardinality: 97 distinct values High Cardinality
Dataset Insights
SM4_Autre has a high cardinality: 201 distinct values High Cardinality
VI2_Autre has a high cardinality: 63 distinct values High Cardinality
VI3 has a high cardinality: 51 distinct values High Cardinality
VI4 has a high cardinality: 132 distinct values High Cardinality
VI6_Autre has a high cardinality: 91 distinct values High Cardinality
VI7 has a high cardinality: 80 distinct values High Cardinality
VI8 has a high cardinality: 224 distinct values High Cardinality
VI8_Autre has a high cardinality: 66 distinct values High Cardinality
TB6 has a high cardinality: 55 distinct values High Cardinality
AO8 has a high cardinality: 69 distinct values High Cardinality
Dataset Insights
AD3 has a high cardinality: 67 distinct values High Cardinality
SS7_Autre has a high cardinality: 55 distinct values High Cardinality
SS12 has a high cardinality: 76 distinct values High Cardinality
ES3b has constant value "Je ne sais pas" Constant
ES4b has constant value "Je ne sais pas" Constant
Date_Enreg has constant length 10 Constant Length
CS1 has constant length 9 Constant Length
Age has constant length 2 Constant Length
ES3b has constant length 14 Constant Length
ES4b has constant length 14 Constant Length
Dataset Insights
ES5 has constant length 3 Constant Length
ES6 has constant length 3 Constant Length
ES8 has constant length 3 Constant Length
AS2 has constant length 3 Constant Length
BD2 has constant length 3 Constant Length
BD3 has constant length 3 Constant Length
BD4 has constant length 3 Constant Length
SM6 has constant length 3 Constant Length
SM7 has constant length 3 Constant Length
VI1 has constant length 3 Constant Length
Dataset Insights
VI5 has constant length 3 Constant Length
TB1 has constant length 3 Constant Length
TB3 has constant length 3 Constant Length
TB7 has constant length 3 Constant Length
AO4 has constant length 3 Constant Length
AO9 has constant length 3 Constant Length
CN1 has constant length 3 Constant Length
CN3 has constant length 3 Constant Length
CN7 has constant length 3 Constant Length
AD2 has constant length 3 Constant Length
Dataset Insights
VI3_Autre has all distinct values Unique
VI7_Autre has all distinct values Unique
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